A random forest-based framework for 3D kidney segmentation from dynamic contrast-enhanced CT images

Khalifa, Fahmi; Soliman, Ahmed; Dwyer, Amy C.; Gimel’farb, Georgy; El-Baz, Ayman

doi:10.1109/icip.2016.7532990

Cited by 16 publications

(8 citation statements)

References 23 publications

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“…Patch-based methods, on the other hand, have gained increased attention in image labeling, since they can alleviate the need for high registration accuracy through exploring several neighboring patches within a local search region [22][23][24][25][26][27]. For such methods, affine registration of the atlases to the target image is often used.…”

Section: Introductionmentioning

confidence: 99%

Automatic brain labeling via multi-atlas guided fully convolutional networks

Fang

Zhang

Nie

et al. 2019

Medical Image Analysis

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Multi-atlas-based methods are commonly used for MR brain image labeling, which alleviates the burdening and time-consuming task of manual labeling in neuroimaging analysis studies. Traditionally, multi-atlas-based methods first register multiple atlases to the target image, and then propagate the labels from the labeled atlases to the unlabeled target image. However, the registration step involves non-rigid alignment, which is often time-consuming and might lack high accuracy. Alternatively, patch-based methods have shown promise in relaxing the demand for accurate registration, but they often require the use of hand-crafted features. Recently, deep learning techniques have demonstrated their effectiveness in image labeling, by automatically learning comprehensive appearance features from training images. In this paper, we propose a multi-atlas guided fully convolutional network (MA-FCN) for automatic image labeling, which aims at further improving the labeling performance with the aid of prior knowledge from the training atlases. Specifically, we train our MA-FCN model in a patch-based manner, where the input data consists of not only a training image patch but also a set of its neighboring (i.e., most

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Section: Introductionmentioning

confidence: 99%

Automatic brain labeling via multi-atlas guided fully convolutional networks

Fang

Zhang

Nie

et al. 2019

Medical Image Analysis

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“…Thus, it is an extension of the conventional Potts model [ 43 ], differing only in that the potentials are estimated analytically. For more mathematical details about our higher-order spatial model, please see [ 33 , 44 ]. Similar to the other features, three spatial-based features were used: the local spatial probability at p and the average probabilities for a 3D cube and a 3 × 3 window centered around p .…”

Section: Methodsmentioning

confidence: 99%

“…To account for these limitations, we developed a 3D kidney segmentation framework that integrates, in addition to the current CT appearance features, higher-order appearance models and adaptive shape model features into a random forests (RF) classification model [ 33 ]. The integrated features increase the ability of our framework to account for the large CT images' inhomogeneities and therefore accurately segment both contrast and noncontrast CTs.…”

Section: Introductionmentioning

confidence: 99%

“…A detailed description of our developed methodology for kidney segmentation from dynamic CT images including the details of the discriminative features is given in Section 2 . It is worth mentioning that, in addition to our methodology presentation in [ 33 ], this paper provides (i) a more comprehensive review of the related literature work on the abdominal CT images segmentation ( Section 3 ); (ii) detailed description of the metrics that are used for segmentation evaluation of our and compared techniques ( Section 3 ); and (iii) expansion of the experimental results by adding an essential metric that is used to evaluate the robustness of segmentation techniques, namely, the receiver operating characteristics (ROC) ( Section 4 ).…”

Section: Introductionmentioning

confidence: 99%

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3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models

Khalifa

Soliman

Elmaghraby

et al. 2017

Computational and Mathematical Methods in Medicine

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Kidney segmentation is an essential step in developing any noninvasive computer-assisted diagnostic system for renal function assessment. This paper introduces an automated framework for 3D kidney segmentation from dynamic computed tomography (CT) images that integrates discriminative features from the current and prior CT appearances into a random forest classification approach. To account for CT images' inhomogeneities, we employ discriminate features that are extracted from a higher-order spatial model and an adaptive shape model in addition to the first-order CT appearance. To model the interactions between CT data voxels, we employed a higher-order spatial model, which adds the triple and quad clique families to the traditional pairwise clique family. The kidney shape prior model is built using a set of training CT data and is updated during segmentation using not only region labels but also voxels' appearances in neighboring spatial voxel locations. Our framework performance has been evaluated on in vivo dynamic CT data collected from 20 subjects and comprises multiple 3D scans acquired before and after contrast medium administration. Quantitative evaluation between manually and automatically segmented kidney contours using Dice similarity, percentage volume differences, and 95th-percentile bidirectional Hausdorff distances confirms the high accuracy of our approach.

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“…In addition to three kidney segmentation categories introduced in [1], over the last decade a body of literature has focused on machine learning approaches such as deep learning and ensemble learning (such as random forest) methods to address this problem. Khalifa et al [3] , [4] designed a 3-D kidney segmentation algorithm using a random forest classifier and adaptive shape modeling. Wolz et al [5], [6] introduced a hierarchical subject-specific atlas generation model to address high inter-subject variability.…”

Section: Introductionmentioning

confidence: 99%

Automated Kidney Segmentation for Traumatic Injured Patients through Ensemble Learning and Active Contour Modeling

Farzaneh

Soroushmehr

Patel

et al. 2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Traumatic abdominal injury can lead to multiple complications including laceration of major organs such as kidneys. Contrast-enhanced Computed Tomography (CT) is the primary imaging modality for evaluating kidney injury. However, the traditional visual examination of CT scans is time consuming, non-quantitative, prone to human error, and costly. In this work we propose a kidney segmentation method using machine learning and active contour modeling. We first detect an initialization mask inside the kidney and then evolve its boundary. This model is specifically developed and evaluated on trauma cases. Our experimental results show the average recall score of 92.6% and average Dice similarity value of 88.9%.

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A random forest-based framework for 3D kidney segmentation from dynamic contrast-enhanced CT images

Cited by 16 publications

References 23 publications

Automatic brain labeling via multi-atlas guided fully convolutional networks

Automatic brain labeling via multi-atlas guided fully convolutional networks

3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models

Automated Kidney Segmentation for Traumatic Injured Patients through Ensemble Learning and Active Contour Modeling

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